:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Li, Xi-Lin
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control
Online Access:	https://arxiv.org/abs/2402.11858
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Hessian-Aware Stochastic Differential Equation for Modelling SGD
by: Li, Xiang, et al.
Published: (2024)

Achieving ${O}(ε^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
by: Yang, Yifan, et al.
Published: (2023)

Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
by: Bylinkin, Dmitry, et al.
Published: (2024)

Gradient-Normalized Smoothness for Optimization with Approximate Hessians
by: Semenov, Andrei, et al.
Published: (2025)

Towards Quantifying the Hessian Structure of Neural Networks
by: Dong, Zhaorui, et al.
Published: (2025)

Schrödinger Bridge Over A Compact Connected Lie Group
by: Mahmood, Hamza, et al.
Published: (2026)

Optimal Potential Shaping on SE(3) via Neural ODEs on Lie Groups
by: Wotte, Yannik P., et al.
Published: (2024)

Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
by: Yu, Qian, et al.
Published: (2024)

SGD with Partial Hessian for Deep Neural Networks Optimization
by: Sun, Ying, et al.
Published: (2024)

Quantitative Convergences of Lie Group Momentum Optimizers
by: Kong, Lingkai, et al.
Published: (2024)

Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization
by: Huang, Feihu
Published: (2024)

Newton-CG methods for nonconvex unconstrained optimization with Hölder continuous Hessian
by: He, Chuan, et al.
Published: (2023)

Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points
by: Yamamoto, Naoya, et al.
Published: (2025)

Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction
by: Feng, Jie, et al.
Published: (2024)

Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms
by: Godichon-Baggioni, Antoine, et al.
Published: (2024)

The Jacobian and Hessian of the Kullback-Leibler Divergence between Multivariate Gaussian Distributions (Technical Report)
by: Maroñas, Juan
Published: (2025)

Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm
by: Yao, Wei, et al.
Published: (2024)

Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits
by: Sadiev, Abdurakhmon, et al.
Published: (2025)

Stochastic Approximation with Block Coordinate Optimal Stepsizes
by: Jiang, Tao, et al.
Published: (2025)

First-ish Order Methods: Hessian-aware Scalings of Gradient Descent
by: Smee, Oscar, et al.
Published: (2025)

On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)

Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-loop and Hessian-free Solution Strategy
by: Liu, Risheng, et al.
Published: (2024)

A Hessian-Free Actor-Critic Algorithm for Bi-Level Reinforcement Learning with Applications to LLM Fine-Tuning
by: Zeng, Sihan, et al.
Published: (2026)

NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information
by: Tang, Wei-Ting, et al.
Published: (2025)

Quadratic Gradient: A Unified Framework Bridging Gradient Descent and Newton-Type Methods by Synthesizing Hessians and Gradients
by: Chiang, John
Published: (2022)

An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
by: Orvieto, Antonio, et al.
Published: (2024)

Asynchronous and Stochastic Distributed Resource Allocation
by: Li, Qiang, et al.
Published: (2025)

Improved Learning Rates for Stochastic Optimization
by: Li, Shaojie, et al.
Published: (2021)

Stochastic-Constrained Stochastic Optimization with Markovian Data
by: Kim, Yeongjong, et al.
Published: (2023)

Robust Out-of-Distribution Stochastic Optimization
by: Li, Xianyu, et al.
Published: (2026)

Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders
by: Bruns-Smith, David, et al.
Published: (2023)

Momentum Does Not Reduce Stochastic Noise in Stochastic Gradient Descent
by: Sato, Naoki, et al.
Published: (2024)

Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to $K$-Level Stochastic Optimizations
by: Pan, Xiaokang, et al.
Published: (2024)

Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
by: O'Leary-Roseberry, Thomas, et al.
Published: (2024)

An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method
by: Zhang, Jiahao, et al.
Published: (2024)

StoTAM: Stochastic Alternating Minimization for Tucker-Structured Tensor Sensing
by: Li, Shuang
Published: (2026)

Training Autoencoders Using Stochastic Hessian-Free Optimization with LSMR
by: Emirahmetoglu, Ibrahim, et al.
Published: (2025)

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2024)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)

A General Continuous-Time Formulation of Stochastic ADMM and Its Variants
by: Li, Chris Junchi
Published: (2024)